A Fresh Produce Industry Playbook
From Gut Feel to Data-Driven
Most produce operations are not behind on AI. They are behind on data. This is a field guide to closing the gap between the question you need answered and the system that could answer it.
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Who this is for
You are probably in one of three places right now.
“We’re already building something.”
IT has a side project going, built with real initiative and a certificate or two earned along the way. That is not the same attention and skill your AI program will need at production scale, without tools designed for produce companies.
“I don’t know what any of this means.”
You are hearing AI everywhere. Your buyers are using it. Your competitors claim they are. You do not have a data analyst on staff and you are not sure whether this applies to you.
“I need to figure this out, but where?”
You have data. You have systems. You just cannot get answers out of them fast enough to matter.
This playbook is for all three. The goal is not to sell you on a particular path. It is to give you enough to ask better questions of your IT team, your vendors, and yourself.
The spreadsheet is the symptom, not the problem.
Here is a conversation that happens in fresh produce operations constantly.
A major retailer calls and wants to know your week-by-week unit commitments for strawberries through week 36. Your person on the phone knows the answer lives somewhere, in the ERP, in a grower tracker, in the spreadsheet someone built two seasons ago that only they fully understand. But there is no fast way to get it. The call ends with “let me get back to you.”
That is the gap. Not that the data does not exist. It does. It is that by the time you can pull it together, format it, and send it, the conversation has moved on.
This plays out differently in every operation. At some companies a massive, overgrown spreadsheet that stalls your computer runs supply chain and inventory. It works, and one person maintains it, but it is fragile the moment that person is out. At others it is a stack of reports pulled by hand from an ERP with a Windows 95 interface. At others it is grower estimates that live in someone’s head, adjusted every season by institutional knowledge that was never written down, and now that person is retiring after 45 years.
The data exists. The problem is that it is stranded.
The buy side is not waiting for you.
While growers and shippers are pulling reports manually, the retail side has been building infrastructure. They have whole dedicated data science teams. They have velocity data. They have shrink. They know which SKUs are pulling and which are sitting, by region, by week.
When your buyer walks into a negotiation, they have a dashboard. When you walk in, you have experience and a spreadsheet you had to pull together that morning.
That gap is real. It is widening. And the window to close it is open right now, because the tools that used to require a team of engineers and a $1.5M infrastructure build are now accessible at a fraction of that cost. But it will not stay open. Once the market consolidates around a new set of practices, the laggards will not catch up. That is what happened in retail. It is what is happening on the supply side now.
The thing nobody is saying out loud.
Most produce operations are behind on AI, and many do not realize it. They are also behind on data, and do not realize that either. The reason is not a lack of effort, and it is not that their tools are wrong for the industry. It is that the person they trust with data was trained for one kind of work, and connecting AI to messy operational data is another kind entirely. This is no knock on them. You would not hand your family doctor a scalpel and prep them for open heart surgery, no matter how good they are. It is a different profession.
Your ERP is powerful. It is also designed for accountants, not sales managers. Your Power BI dashboards are clean, and for what they were built to do, they are good. The catch is they answer a fixed set of questions. Ask a new one and you are waiting on the next build. Your retail portal data is available. Nobody is systematically looking at it.
Even the crop estimates in your planning system were built on guesses, submitted by people with a reason to round up.
None of this is anyone’s fault. It is just that data infrastructure in this industry has been built for reporting, not for decisions. Those are different problems.
There are different kinds of AI. They do different things.
When most produce companies say they are “doing AI,” they mean one of two things: they bought Microsoft Copilot licenses, or they are letting employees use ChatGPT. Both are legitimate tools. Neither of them is what makes an operation data-driven. Here is the difference.
Task-Oriented AI
Copilot, ChatGPT
Designed to help you work faster on tasks you are already doing. Drafting an email. Summarizing a document. Generating a presentation. With the right configuration, some can even connect to data sources like Fabric, pull from spreadsheets, or reach business data through integrations.
Access is part of the problem, and reliability is the rest. Even when a tool can reach your data, what is missing is the context, the guardrails, and the produce-specific knowledge that make answers trustworthy enough to act on. A tool that can query your ERP but does not understand what a ship week means, or why your crop year does not follow a calendar year, gives you confident answers that are wrong in ways you will not catch.
Data-Connected AI
Built for your business
A different architecture and a different intent. Not just connecting a general model to your data, but building in the business context, the industry terminology, the org-specific rules, and the guardrails that make outputs reliable enough to actually change decisions. It is built to know what a ship week is and why your crop year does not follow a calendar.
The key line: productivity AI does. Data-connected AI thinks. One moves faster through the work you already do. The other reasons through your own data, in the language of your business, to help you make a better call.
The handoff from IT to specialty
The next step is not about effort. It is about specialty. Using AI reliably for operational data is its own discipline. It draws on understanding unique identifiers, building a data layer that can serve as the foundation for intelligence, not just dashboards. It is knowing the difference between a semantic layer and a data strategy. It also means knowing the specific vocabulary of your business well enough to build guardrails around the answers.
It helps to name the distinction. A skilled data analyst understands the question and turns clean data into reports and answers. That skill will make or break your margins. But connecting AI to messy operational data calls for a different toolkit: data architecture, LLM integration, prompt engineering, and code management. Most IT professionals have not built something that works this way, let alone something production-ready.
Side projects and learning are great. Just not what you run a business on.
Four questions worth asking your IT lead
- 1How does the LLM decide, and on what information?
- 2How do you control the logic the LLM uses to get an answer?
- 3How does the LLM know the terminology I use?
- 4How does the LLM remember what I care about?
The answers to those four questions will tell you more about your actual AI readiness than any certification or vendor demo.
Three stages. Most of the industry is at Stage 1.
That is the starting point, not the destination.
Stage 1
Describing Symptoms
You know something is wrong. You can describe the pain: a margin call that went sideways, a pack schedule off by a week, a pricing decision made without full supply visibility. But the data to understand why, and to catch the next one, is not accessible fast enough to be useful.
You’re probably here if
- Your data lives in three or more systems that do not talk to each other
- Season recaps take more than a day to pull together
- Market intelligence comes primarily from phone calls
- Pricing decisions are validated by experience rather than data
- One person owns the reporting and nobody else can easily replicate it
~80%
of fresh produce operations. Not failure. A starting point.
Stage 2
Diagnosis
You can see what is happening and, increasingly, what is likely to happen next. Data sources are connected or being connected. Someone owns analytics as a real function. The questions being asked are forward-looking.
Most of the ROI in this transition lives here. Not Stage 3. Catching a market softening a week early. Identifying a fill-rate problem before it costs a season’s margin. Spotting a retail program that looks profitable on paper but is not.
You’re moving here if
- Someone owns analytics; it is their job, not a side task
- You can answer a margin question in hours, not days
- Data sources are connected or being actively connected
- The team asks forward-looking questions, not just backward-looking ones
Stage 3
Treatment Plan
The system tells you what to do. Forecasting models run regularly and the team trusts them enough to act. Recommendations come out of the data. Decisions across the operation are influenced by shared information, not siloed judgment.
You’re here if
- Systems generate recommendations, not just reports
- Forecasting models run and drive real decisions
- The team trusts the data enough to act against their gut when the data says to
Very few produce operations are here yet. The buy side is moving from Stage 2 into Stage 3. Build Stage 2 right, and Stage 3 is closer than most people think.
One honest note about Stage 3
The temptation is to buy a platform and skip to Stage 3 because the demo is impressive. That almost never works. The operations that get there built Stage 2 first. They identified the one question that mattered most, found the data to answer it, and made that loop reliable before adding complexity.
A 90-day proof of concept on one question beats an 18-month platform rollout every time.
You already have most of the data you need. The gap is the bridge.
A recurring frustration in produce: smart, experienced people making significant decisions on incomplete information. Not because the information does not exist, but because nobody has built the bridge between what the systems hold and what the decision-maker needs. Here are the questions that should be answerable in your operation. If they are not, that is the gap.
Sales & Marketing
- What is the real demand signal in the next two to four weeks, not what is committed, but what the market will actually absorb?
- Which retail programs are profitable after freight, labor, and actual pack-out performance?
- Where are we leaving margin on the table?
Operations & Pack
- What should the pack schedule look like based on projected demand, not just committed orders?
- What is the real cost per unit by SKU once you account for pack line performance, rework, and actual labor?
- Where are the bottlenecks that will surface in two weeks if nothing changes today?
Supply & Procurement
- What does the 30 to 60 day supply picture look like against committed demand?
- What are competitors likely packing right now, and what does that mean for market availability?
- Where is the exposure to a supply-demand imbalance before it shows up in pricing?
Leadership
The two that matter most
Do we have the grower base and the mix we need for where the market is going?
If our best analyst left tomorrow, would anyone else know how to pull the numbers we depend on?
Every one of these questions is answerable with data most operations already have. What is missing is not the data. It is the bridge.
The market tells you what's coming. Most operations aren't listening.
The most dangerous assumption in produce is that if something important were happening, you would know about it. Usually you find out when the price drops, not before. Here is what the signal looks like before the market moves.
Harvest volumes accelerating faster than demand
Shows up in USDA shipment reports and import data before it shows up in pricing. Most operations are not watching it systematically enough to act before the floor drops.
Multiple shippers packing heavy at once
Everyone reads the same market signal and responds the same way. The collective response creates the oversupply they were each trying to avoid. Visible in aggregate, but only if you pull data beyond your own operation.
Spot pricing softening 7 to 10 days early
The spot market is a leading indicator. When spot prices start softening, committed demand is not absorbing available supply. Most operations do not catch this until they are already on the wrong side of it.
Promo windows misaligned with supply peaks
Promotional commitments are made weeks in advance. When the actual supply peak does not align, because of weather, harvest timing, or competitive supply, you are either sitting on product or scrambling to cover at the wrong price.
Inventory aging while you still pack to forecast
When cooler inventory is aging and the pack schedule has not responded, the information loop is broken. Not the data. The loop.
The signals most operations never collect
Retail pull-through velocity
Your own customers publish this through portals you already have. It tells you whether a program is working before the order gets cut, while you can still do something about it.
Competitor supply patterns
You cannot see what competitors are packing directly, but the shape of it shows up in terminal market reports, spot pricing, and import data. It takes connecting a few sources and knowing what to look for.
Weather in competing regions
A warm front in another growing region can pull a harvest forward by 10 to 14 days. If you are not watching weather there, you find out when the product hits the market, not before.
The retail data your buyer has
Syndicated services like Nielsen give the buy side a category-wide picture of velocity, distribution, and trends. Some is available to you, and some buyers will share it if you ask. Most produce companies never have.
A checklist for evaluating any AI tool.
All three can work. What doesn't work is doing nothing.
Path 1
Build Internally
You hire the capability to build and maintain real data infrastructure. Not just a data analyst, but someone with skills in data architecture, LLM integration, prompt engineering, and code management. People with all of it are not easy to find, and they know their market rate.
Right for you if
You genuinely want to run a software team, not just use software. That means owning the hiring, the maintenance, and the constant reinvestment as AI changes, for years, not months, and can hire and retain scarce technical talent in your market.
The honest number
$1M–$1.5M
first two years, with salaries, infrastructure, and the cost of a wrong hire.
Watch out for
A first hire who is technically strong but operationally irrelevant. Building for internal reports rather than decisions. Becoming dependent on one hard-to-replace specialist. AI moves fast, so the system you build today needs continuous investment to stay relevant.
Path 2
Hire a Consultant
Bring in outside expertise to build or configure a solution for your specific situation.
Right for you if
You need to move faster than a full internal build, have specific questions to answer, and can find someone who understands both data architecture and produce operations. That last part is a small pool.
The honest number
$600K–$1M
consulting hours and build cost only. Infrastructure, hosting, tooling, and ongoing maintenance are separate and paid by you.
Watch out for
Strong technical execution that does not translate to operational insight. Projects that end when the consultant leaves with no internal capacity to maintain them. Scope creep from one well-defined question to five. Ask directly: what does ongoing maintenance look like, and who owns it after they leave?
Path 3
Use a Platform
Subscribe to a purpose-built tool that connects to your data and delivers analytics without requiring you to build the infrastructure yourself.
Right for you if
You want the fastest path to value and want a strategic partner, and the platform was built specifically for produce, not a generic analytics tool repositioned for the industry.
The honest number
$75K–$250K / yr
infrastructure, warehouse, and semantic layer included. A fraction of what it costs to build equivalent capability internally. First value in 30 to 90 days if the data connection is straightforward.
Watch out for
Generic analytics tools dressed up for produce. Reporting platforms that generate better dashboards without changing decisions. A vendor that hands you a login but no partner to onboard the tool into your business so it actually understands how you operate.
Paths 1 & 2
Building it is only half the job. Someone has to keep running it. AI capabilities go stale fast, so a system that is impressive at launch needs steady upkeep to stay useful. Before you commit, ask the hard maintenance questions: when the consultant leaves, can your team actually operate what they built? Does the person you hire have the ongoing capacity to maintain and improve it, not just stand it up once? If the answer is no, you have built another fragile system, just a more expensive one.
The principle that applies to all three
Pick one question first. Not five. The highest-value question your operation needs to answer.
Find the data that answers it. You probably already have 80% of it. Connect it. Build the simplest version that works and perfect it. Then decide what to build next. The operations that stall are trying to solve everything at once. The ones that make progress pick one problem and prove it can be solved.
Five things you can do this week.
No budget required.
- 1
Write down your one question.
The single highest-value question your operation needs to answer. Not a list. One. The constraint is the point. If you have five questions, you do not have a priority. A good one is forward-looking, tied to a decision you make regularly, and currently answered by a guess or a phone call. Write it down. Share it with one other person. See if they agree it is the right one.
- 2
Inventory where your data lives.
Budget a few hours, maybe a full day. Go through every system that touches your operation, ERP, pack line, retail portals, third-party subscriptions, spreadsheets, grower trackers, and write down what is in each, how current it is, and who has access. You are not solving anything yet. You are mapping the terrain. Most are surprised by two things: how much data they have, and how many people maintain parallel versions of the same information.
- 3
Identify who's making decisions without data.
Walk the operation. Talk to the people who act on data, not the ones who collect it. Ask: “What was the last significant decision you made, and what did you base it on?” You are looking for two things: decisions made on instinct where data exists but is not used, and decisions made on data nobody has verified. Both are common. Both are fixable.
- 4
Have one honest conversation with leadership.
Frame it in dollars, not technology. Not “we need a better analytics stack,” which stalls in budget discussions. More like: “We made this decision last season without complete information. If we had the data, we would have done this differently. Here is roughly what that cost us.” The number does not have to be precise. It has to be credible. The goal is not budget approval. It is to establish that this is a business problem, not a technology project.
- 5
Ask your IT lead four questions.
From Section 2, worth repeating because most executives have never actually asked:
- How does the LLM decide, and on what information?
- How do you control the logic the LLM uses to get an answer?
- How does the LLM know the terminology I use?
- How does the LLM remember what I care about?
This is not about putting anyone on the spot. It is about knowing where you really stand when you peek behind the reports.
Closing
Start with the right question, not the wrong platform.
Getting data-driven in produce is real work. It takes investment, the right people, and more time than most vendors will tell you. That is not a reason not to start. It is a reason to start with the right question instead of the wrong platform.
The first step is always the same: one question, clearly defined, with the data to answer it. That does not require a budget line or a new hire. It requires being honest about what you do not currently know, and deciding it is worth finding out.
Start there.
Ask a question. Get an answer.
Huckleberry Signals is an AI analyst built for the produce industry. Reach out anytime.
Amanda Kuelker
amanda.kuelker@huckleberrysignals.ai